More and more companies are investigating skill management platforms. Virtually any company that relies on the skills of its people to deliver services, develop innovations and create value for its customers needs to be able to answer critical business questions on skills.

Skill management platforms and systems play a role in differentiated talent in organizations. For more on skill management, see our post on what is skill management

1. What skills are available to our organization?

2. How are they being applied?

3. Do we have the skills we need to meet today’s goals?

4. Will we have the skills we need to meet tomorrow’s goals?

5. Are their hidden pockets of potential we can deploy?

6. Who are the critical people on our team?

What are the options other than a skill management platform?

To answer this, let’s look at what a skill management platform should do. A good skill management platform needs the following capabilities:

• A rich skill model that connects a skill to its associated and complementary skills

• A way to connect skills to work - roles, projects, outcomes, career highlights

• The ability to uncover potential skills and find skill gaps

• Integrations with other systems to exchange data about availability, learning, project experience

What are the options? The three that we see most frequently are

1. a spreadsheet or simple database, filled out occasionally by hand

2. a collection of résumés managed in some form of document storage system like SharePoint

3. the talent management or performance management system.

Let's look at the advantages and disadvantages of each approach.

Skills managed through spreadsheets and databases

In general, people use these because they have to have something and a spreadsheet can be pulled together by just about anyone. In fact, companies that rely on spreadsheets for skill management tend to have lots of them, some created by HR, others by operations. These spreadsheets (and I have reviewed a lot of them) are generally not easy to integrate. They each have different structures and skill taxonomies. They also tend to be incomplete and out of date, generating more confusion than insight. There is no way to effectively manage skills using a disconnected set of databases, even if these were updated every week. Companies relying on this approach are set up to fail.

Talent management systems (and other systems that treat skills as an add on)

Many HR systems have some form of vestigial skill management system. Often this is a simply a list of skills with a ranking. Sometimes these are self ranking, sometimes they are given by management. In 360 reviews, there can be multiple rankings. Learning Management Systems (LMS) will often have a way of linking skills to learning objectives and even to courses. There is an implicit assumption, usually wrong, that training and tests are an adequate way to assess skills. They are not. Training, and even formal certifications, are proxies for skills. Just one form of evidence. The only real proof of a skill is in the context of work.

Résumés, project records and a content management system

One of the most common solutions for skill management has been to have résumés in a content repository like SharePoint and then to rely on search to find people when you need them. This is more or less useless as the résumés are generally out of date and there is no effective way to get a picture of skills at different levels of the organization. Some of hope though, that new AI technologies will come to the rescue. Dump résumés, project records, all sorts of other records into a system like SharePoint and run some advanced AI and pattern recognition software across this and see what you find. Sounds crazy but there is a lot of potential in this approach.

Getting real insight into skills and how they are being applied requires integration of information from multiple sources. Résumés are not enough but content management systems are not limited to just résumés. Many other types of content can be included. Think of this as a kind of mash up of  information about people and knowledge management. Of course the content alone is not enough. The success of this approach depends on the ability of the AI to find patterns across documents and to answer the critical business questions on skills.

There are three challenges to the content management plus pattern recognition approach to skills management.

1. In the absence of a rich underlying skill model, like the Ibbaka Talent Skill Graph, can the AI actually provide meaningful answers?

2. Can the content for any one company provide a rich enough dataset for the AI to be effective?

3. Can the content management system support the sort of rich social interactions around skills needed to drive engagement, keep information up-to-date?

My own experience is that the answer to all three of these questions is 'No.'

The primary reason for this is the absence of a data model that can make meaningful connections between the data. Ibbaka uses the Skill Graph for this purpose. The Ibbaka Talent Skill Graph connects skills to other skills (this is critical, understanding the relationships between skills is necessary to uncovering potential skills and for accurate estimations of expertise), how skills are connected to roles and projects, and how skills connect people into teams. The Ibbaka Talent skill graph has been built using our Skill Inference technology and integrates data from multiple sources - surveys, existing skill models, data from many websites, user actions. Without this deep, connected dataset the AIs will not work effectively.

If answering the critical business questions around skills matters to your organization you need a skill management platform. A platform informed by a rich dataset, integrations with other content and applications, and an engaging user experience. The shortcuts, hacks and old standbys will not work in today's dynamic world of work.